CAPSTONE PROJECT - CAR DETECTION

COMPUTER VISION PROJECT- PROJECT BY GROUP 8

Step 1: Import the data

import numpy as np
import plotly.express as px
import os
import zipfile
import cv2
from PIL import Image
from IPython.display import display
import ipywidgets as widgets
from matplotlib import pyplot as plt
import matplotlib.patches as patches
from collections import defaultdict
from io import StringIO
from PIL import Image
import random
from IPython.display import display
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline
import warnings
import pickle
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import plotly.graph_objs as go
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Dropout
from tensorflow.keras.callbacks import EarlyStopping
warnings.filterwarnings("ignore")

car_data_zip_path = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/Car+Images.zip'
annotation_zip_path = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/Annotations.zip'

destination_folder = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites'

if not os.path.exists(destination_folder):
    os.makedirs(destination_folder)

with zipfile.ZipFile(car_data_zip_path, 'r') as car_data_zip:
    car_data_zip.extractall(os.path.join(destination_folder, 'car_data_extracted'))

with zipfile.ZipFile(annotation_zip_path, 'r') as annotation_zip:
    annotation_zip.extractall(os.path.join(destination_folder, 'annotations_extracted'))

print("Data extracted and stored in the destination folder.")

Data extracted and stored in the destination folder.

carmodel_file = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/Car+names+and+make.csv'
car_data = pd.read_csv(carmodel_file)
print("Car Data:")
print(car_data.head())

Car Data:
  AM General Hummer SUV 2000
0        Acura RL Sedan 2012
1        Acura TL Sedan 2012
2       Acura TL Type-S 2008
3       Acura TSX Sedan 2012
4  Acura Integra Type R 2001

Step 2: Map training and testing images to its classes

def process_images(folder_path):
    data = []

    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
                car_name = os.path.basename(root)
                car_model = car_name.split()[-1]
                car_model_1 = ' '.join(car_name.split()[:-1])
                image_name = file
                image_location = os.path.join(root, file)

                data.append({
                    'carName': car_name,
                    'carModel': car_model,
                    'carModel_1': car_model_1,
                    'Image Name': image_name,
                    'Image Location': image_location
                })

    return data

# Defining the path to the main folder containing subfolders with images
Train_image_folder = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Train Images'

# Processing images recursively
Train_data = process_images(Train_image_folder)

# Creating a DataFrame
Train_data_df = pd.DataFrame(Train_data)

# Displaying the final DataFrame with image locations
Train_data_df.head()

	carName	carModel	carModel_1	Image Name	Image Location
0	Acura Integra Type R 2001	2001	Acura Integra Type R	00198.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
1	Acura Integra Type R 2001	2001	Acura Integra Type R	00255.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
2	Acura Integra Type R 2001	2001	Acura Integra Type R	00308.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
3	Acura Integra Type R 2001	2001	Acura Integra Type R	00374.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
4	Acura Integra Type R 2001	2001	Acura Integra Type R	00878.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...

def process_images(folder_path):
    data = []

    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
                car_name = os.path.basename(root)
                car_model = car_name.split()[-1]
                car_model_1 = ' '.join(car_name.split()[:-1])
                image_name = file
                image_location = os.path.join(root, file)

                data.append({
                    'carName': car_name,
                    'carModel': car_model,
                    'carModel_1': car_model_1,
                    'Image Name': image_name,
                    'Image Location': image_location
                })

    return data

# Defining the path to the main folder containing subfolders with images
Test_image_folder = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Test Images'

# Processing images recursively
Test_data = process_images(Test_image_folder)

# Creating DataFrame
Test_data_df = pd.DataFrame(Test_data)

# Displaying the final DataFrame
Test_data_df.head()

	carName	carModel	carModel_1	Image Name	Image Location
0	Acura Integra Type R 2001	2001	Acura Integra Type R	00128.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
1	Acura Integra Type R 2001	2001	Acura Integra Type R	00130.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
2	Acura Integra Type R 2001	2001	Acura Integra Type R	00386.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
3	Acura Integra Type R 2001	2001	Acura Integra Type R	00565.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...
4	Acura Integra Type R 2001	2001	Acura Integra Type R	00711.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...

Lets visualize few images

a : Train Images

Train_data_df.shape

(8144, 5)

for index, row in Train_data_df.head(5).iterrows():
    image_path = row['Image Location']
    img = Image.open(image_path)
    plt.imshow(img)
    plt.title(f'Image - {row["Image Name"]}\nLocation: {row["Image Location"]}')
    plt.show()

b : Test Images

Test_data_df.shape

(8041, 5)

for index, row in Test_data_df.head(5).iterrows():
    image_path = row['Image Location']
    img = Image.open(image_path)
    plt.imshow(img)
    plt.title(f'Image - {row["Image Name"]}\nLocation: {row["Image Location"]}')
    plt.show()

Step 3: Map training and testing images to its annotations

train_annotations = pd.read_csv("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/annotations_extracted/Annotations/Train Annotations.csv")
test_annotations = pd.read_csv("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/annotations_extracted/Annotations/Test Annotation.csv")

train_annotations

	Image Name	Bounding Box coordinates	Unnamed: 2	Unnamed: 3	Unnamed: 4	Image class
0	00001.jpg	39	116	569	375	14
1	00002.jpg	36	116	868	587	3
2	00003.jpg	85	109	601	381	91
3	00004.jpg	621	393	1484	1096	134
4	00005.jpg	14	36	133	99	106
...	...	...	...	...	...	...
8139	08140.jpg	3	44	423	336	78
8140	08141.jpg	138	150	706	523	196
8141	08142.jpg	26	246	660	449	163
8142	08143.jpg	78	526	1489	908	112
8143	08144.jpg	20	240	862	677	17

8144 rows × 6 columns

Lets remane the headings appropriately for the train data!

train_annotations.rename(columns = {'Bounding Box coordinates':'xmin'}, inplace = True)
train_annotations.rename(columns = {'Unnamed: 2':'ymin'}, inplace = True)
train_annotations.rename(columns = {'Unnamed: 3':'xmax'}, inplace = True)
train_annotations.rename(columns = {'Unnamed: 4':'ymax'}, inplace = True)
train_annotations.rename(columns = {'Image class':'Image_class'}, inplace = True)
train_annotations.rename(columns = {'Image Name':'Image Name'}, inplace = True)
train_annotations

	Image Name	xmin	ymin	xmax	ymax	Image_class
0	00001.jpg	39	116	569	375	14
1	00002.jpg	36	116	868	587	3
2	00003.jpg	85	109	601	381	91
3	00004.jpg	621	393	1484	1096	134
4	00005.jpg	14	36	133	99	106
...	...	...	...	...	...	...
8139	08140.jpg	3	44	423	336	78
8140	08141.jpg	138	150	706	523	196
8141	08142.jpg	26	246	660	449	163
8142	08143.jpg	78	526	1489	908	112
8143	08144.jpg	20	240	862	677	17

8144 rows × 6 columns

test_annotations

	Image Name	Bounding Box coordinates	Unnamed: 2	Unnamed: 3	Unnamed: 4	Image class
0	00001.jpg	30	52	246	147	181
1	00002.jpg	100	19	576	203	103
2	00003.jpg	51	105	968	659	145
3	00004.jpg	67	84	581	407	187
4	00005.jpg	140	151	593	339	185
...	...	...	...	...	...	...
8036	08037.jpg	49	57	1169	669	63
8037	08038.jpg	23	18	640	459	16
8038	08039.jpg	33	27	602	252	17
8039	08040.jpg	33	142	521	376	38
8040	08041.jpg	77	73	506	380	32

8041 rows × 6 columns

Lets remane the headings appropriately for the test data!

test_annotations.rename(columns = {'Bounding Box coordinates':'xmin'}, inplace = True)
test_annotations.rename(columns = {'Unnamed: 2':'ymin'}, inplace = True)
test_annotations.rename(columns = {'Unnamed: 3':'xmax'}, inplace = True)
test_annotations.rename(columns = {'Unnamed: 4':'ymax'}, inplace = True)
test_annotations.rename(columns = {'Image class':'Image_class'}, inplace = True)
test_annotations.rename(columns = {'Image Name':'Image Name'}, inplace = True)
test_annotations

	Image Name	xmin	ymin	xmax	ymax	Image_class
0	00001.jpg	30	52	246	147	181
1	00002.jpg	100	19	576	203	103
2	00003.jpg	51	105	968	659	145
3	00004.jpg	67	84	581	407	187
4	00005.jpg	140	151	593	339	185
...	...	...	...	...	...	...
8036	08037.jpg	49	57	1169	669	63
8037	08038.jpg	23	18	640	459	16
8038	08039.jpg	33	27	602	252	17
8039	08040.jpg	33	142	521	376	38
8040	08041.jpg	77	73	506	380	32

8041 rows × 6 columns

Now, Lets merge the dataframe of annotations and image dataframe for the train data!

Train_final_df = pd.merge(Train_data_df, train_annotations, how='inner',left_on='Image Name', right_on='Image Name')

Train_final_df.head(20)

	carName	carModel	carModel_1	Image Name	Image Location	xmin	ymin	xmax	ymax	Image_class
0	Acura Integra Type R 2001	2001	Acura Integra Type R	00198.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	91	121	574	357	6
1	Acura Integra Type R 2001	2001	Acura Integra Type R	00255.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	29	78	734	396	6
2	Acura Integra Type R 2001	2001	Acura Integra Type R	00308.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	16	136	775	418	6
3	Acura Integra Type R 2001	2001	Acura Integra Type R	00374.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	9	184	740	499	6
4	Acura Integra Type R 2001	2001	Acura Integra Type R	00878.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	101	162	882	650	6
5	Acura Integra Type R 2001	2001	Acura Integra Type R	00898.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	89	68	483	310	6
6	Acura Integra Type R 2001	2001	Acura Integra Type R	01010.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	6	3	932	639	6
7	Acura Integra Type R 2001	2001	Acura Integra Type R	01012.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	58	72	763	529	6
8	Acura Integra Type R 2001	2001	Acura Integra Type R	01255.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	17	80	735	492	6
9	Acura Integra Type R 2001	2001	Acura Integra Type R	01617.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	62	150	543	339	6
10	Acura Integra Type R 2001	2001	Acura Integra Type R	01864.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	64	202	898	574	6
11	Acura Integra Type R 2001	2001	Acura Integra Type R	01911.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	91	175	567	410	6
12	Acura Integra Type R 2001	2001	Acura Integra Type R	02095.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	90	189	1495	820	6
13	Acura Integra Type R 2001	2001	Acura Integra Type R	02605.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	10	34	627	322	6
14	Acura Integra Type R 2001	2001	Acura Integra Type R	02738.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	86	292	1176	639	6
15	Acura Integra Type R 2001	2001	Acura Integra Type R	02911.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	64	240	1508	943	6
16	Acura Integra Type R 2001	2001	Acura Integra Type R	02919.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	42	96	612	353	6
17	Acura Integra Type R 2001	2001	Acura Integra Type R	03265.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	33	150	619	409	6
18	Acura Integra Type R 2001	2001	Acura Integra Type R	03608.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	180	205	538	400	6
19	Acura Integra Type R 2001	2001	Acura Integra Type R	03670.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	146	73	486	236	6

train_annotations.iloc[3677]

Image Name     03678.jpg
xmin                  66
ymin                  79
xmax                 224
ymax                 168
Image_class          190
Name: 3677, dtype: object

Train_final_df.shape

(8144, 10)

Now, Lets merge the dataframne of annotations and image dataframe for the test data!

Test_final_df = pd.merge(Test_data_df, test_annotations,how='inner', left_on='Image Name', right_on='Image Name')

Test_final_df.head(20)

	carName	carModel	carModel_1	Image Name	Image Location	xmin	ymin	xmax	ymax	Image_class
0	Acura Integra Type R 2001	2001	Acura Integra Type R	00128.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	122	149	743	455	6
1	Acura Integra Type R 2001	2001	Acura Integra Type R	00130.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	27	74	774	449	6
2	Acura Integra Type R 2001	2001	Acura Integra Type R	00386.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	86	184	723	425	6
3	Acura Integra Type R 2001	2001	Acura Integra Type R	00565.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	15	1	545	347	6
4	Acura Integra Type R 2001	2001	Acura Integra Type R	00711.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	126	65	735	347	6
5	Acura Integra Type R 2001	2001	Acura Integra Type R	01002.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	54	225	499	610	6
6	Acura Integra Type R 2001	2001	Acura Integra Type R	01035.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	25	23	761	529	6
7	Acura Integra Type R 2001	2001	Acura Integra Type R	01176.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	289	433	1462	904	6
8	Acura Integra Type R 2001	2001	Acura Integra Type R	01326.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	40	108	1250	752	6
9	Acura Integra Type R 2001	2001	Acura Integra Type R	01864.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	78	110	410	314	6
10	Acura Integra Type R 2001	2001	Acura Integra Type R	02011.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	30	27	552	306	6
11	Acura Integra Type R 2001	2001	Acura Integra Type R	02182.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	26	132	1009	675	6
12	Acura Integra Type R 2001	2001	Acura Integra Type R	02425.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	37	172	559	412	6
13	Acura Integra Type R 2001	2001	Acura Integra Type R	02433.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	15	21	632	288	6
14	Acura Integra Type R 2001	2001	Acura Integra Type R	02458.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	21	218	771	511	6
15	Acura Integra Type R 2001	2001	Acura Integra Type R	02595.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	55	149	531	359	6
16	Acura Integra Type R 2001	2001	Acura Integra Type R	02742.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	73	210	439	359	6
17	Acura Integra Type R 2001	2001	Acura Integra Type R	02798.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	123	82	371	246	6
18	Acura Integra Type R 2001	2001	Acura Integra Type R	03056.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	99	270	844	583	6
19	Acura Integra Type R 2001	2001	Acura Integra Type R	03466.jpg	C:/Users/SAIF/Desktop/Great Learning/Capstone ...	44	147	595	435	6

Test_final_df.shape

(8041, 10)

Lets have a look at the sanity of the data and take appropriate steps if necessary!

Train_final_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8144 entries, 0 to 8143
Data columns (total 10 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   carName         8144 non-null   object
 1   carModel        8144 non-null   object
 2   carModel_1      8144 non-null   object
 3   Image Name      8144 non-null   object
 4   Image Location  8144 non-null   object
 5   xmin            8144 non-null   int64 
 6   ymin            8144 non-null   int64 
 7   xmax            8144 non-null   int64 
 8   ymax            8144 non-null   int64 
 9   Image_class     8144 non-null   int64 
dtypes: int64(5), object(5)
memory usage: 699.9+ KB

Test_final_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8041 entries, 0 to 8040
Data columns (total 10 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   carName         8041 non-null   object
 1   carModel        8041 non-null   object
 2   carModel_1      8041 non-null   object
 3   Image Name      8041 non-null   object
 4   Image Location  8041 non-null   object
 5   xmin            8041 non-null   int64 
 6   ymin            8041 non-null   int64 
 7   xmax            8041 non-null   int64 
 8   ymax            8041 non-null   int64 
 9   Image_class     8041 non-null   int64 
dtypes: int64(5), object(5)
memory usage: 691.0+ KB

Train_final_df['carModel'].value_counts().index

Index(['2012', '2007', '2009', '2010', '2011', '2008', '1993', '1994', '1998',
       '2001', '2006', '1991', '2000', '2002', '1997', '1999'],
      dtype='object')

Test_final_df['carModel'].value_counts().index

Index(['2012', '2007', '2009', '2010', '2011', '2008', '1993', '1994', '1998',
       '2001', '1991', '2002', '2006', '2000', '1999', '1997'],
      dtype='object')

train_class_percentage = Train_final_df['Image_class'].value_counts(normalize=True) * 100
test_class_percentage = Test_final_df['Image_class'].value_counts(normalize=True) * 100

print("Train Data Class Percentages:")
print(train_class_percentage)

print("\nTest Data Class Percentages:")
print(test_class_percentage)

Train Data Class Percentages:
119    0.834971
79     0.601670
167    0.589391
161    0.589391
144    0.577112
         ...   
175    0.380648
64     0.368369
158    0.356090
99     0.343811
136    0.294695
Name: Image_class, Length: 196, dtype: float64

Test Data Class Percentages:
119    0.845666
161    0.596941
79     0.596941
167    0.584504
43     0.572068
         ...   
175    0.373088
158    0.360652
64     0.360652
99     0.335779
136    0.298470
Name: Image_class, Length: 196, dtype: float64

Lets explore the data by plotting few visualizations and performing EDA.

1 : Test data VS Train Data

total_train_rows = Train_final_df.shape[0]
total_test_rows = Test_final_df.shape[0]
train_percentage = (total_train_rows / (total_train_rows + total_test_rows)) * 100
test_percentage = (total_test_rows / (total_train_rows + total_test_rows)) * 100

sizes = [total_train_rows, total_test_rows]
labels = ['Train Data', 'Test Data']
colors = ['lightcoral', 'lightskyblue']
explode = (0.1, 0)

plt.figure(figsize=(8, 6))
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)
plt.title('Train-Test Data Percentage Split')
plt.axis('equal')
plt.show()

2 : Cars by year of their origin

carModel_counts = Train_final_df['carModel'].value_counts().reset_index()
carModel_counts.columns = ['carModel', 'Count']

plt.figure(figsize=(10, 6))
plt.bar(carModel_counts['carModel'], carModel_counts['Count'], color='skyblue')
plt.xlabel('Car Model')
plt.ylabel('Count')
plt.title('Train data Cars by year of origin')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 6))
plt.bar(carModel_counts['carModel'], carModel_counts['Count'], color='skyblue')
plt.xlabel('Car Model')
plt.ylabel('Count')
plt.title('Test data Cars by year of origin')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

3 : Count of top 10

top_10_carModel_1 = Train_final_df['carModel_1'].value_counts().nlargest(10)
bar_colors = ['skyblue', 'orange', 'green', 'red', 'purple', 'pink', 'yellow', 'cyan', 'magenta', 'lightgreen']
plt.figure(figsize=(10, 6))
plt.bar(top_10_carModel_1.index, top_10_carModel_1.values, color=bar_colors)
plt.xlabel('Car Model_1')
plt.ylabel('Count')
plt.title('Top 10 Train Cars')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

top_10_carModel_1 = Test_final_df['carModel_1'].value_counts().nlargest(10)
bar_colors = ['skyblue', 'orange', 'green', 'red', 'purple', 'pink', 'yellow', 'cyan', 'magenta', 'lightgreen']
plt.figure(figsize=(10, 6))
plt.bar(top_10_carModel_1.index, top_10_carModel_1.values, color=bar_colors)
plt.xlabel('Car Model_1')
plt.ylabel('Count')
plt.title('Top 10 Test Cars')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Step 4 : Display images with bounding box

def display_image_with_bounding_box(img_num):
    img_path = Train_data_df.loc[img_num, 'Image Location']
    img = cv2.imread(img_path)

    xmin = int(Train_final_df.loc[img_num, 'xmin'])
    ymin = int(Train_final_df.loc[img_num, 'ymin'])
    xmax = int(Train_final_df.loc[img_num, 'xmax'])
    ymax = int(Train_final_df.loc[img_num, 'ymax'])

    cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)

    car_model = Train_data_df.loc[img_num, 'carModel_1']

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    plt.figure(figsize=(8, 6))
    plt.imshow(img)
    plt.title(f'Image with Bounding Box - {Train_data_df.loc[img_num, "Image Name"]}')
    plt.axis('off')  # Remove axis
    plt.show()

    print(f'Coordinates: xmin={xmin}, ymin={ymin}, xmax={xmax}, ymax={ymax}')
    print(f'Car Model: {car_model}')

random_indices = random.sample(range(len(Train_data_df)), 5)
for img_num in random_indices:
    display_image_with_bounding_box(img_num)

Coordinates: xmin=108, ymin=37, xmax=682, ymax=555
Car Model: HUMMER H3T Crew Cab

Coordinates: xmin=12, ymin=58, xmax=591, ymax=302
Car Model: Nissan 240SX Coupe

Coordinates: xmin=7, ymin=164, xmax=636, ymax=342
Car Model: Lincoln Town Car Sedan

Coordinates: xmin=53, ymin=215, xmax=723, ymax=517
Car Model: Audi S4 Sedan

Coordinates: xmin=126, ymin=98, xmax=822, ymax=572
Car Model: HUMMER H2 SUT Crew Cab

Step 5 : Design, train and test basic CNN models to classify the car

!pip install tensorflow

Model 1

img_width, img_height = 224, 224
batch_size = 16
train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.1,
                                   zoom_range=0.1,
                                   horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)

train_data_dir = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Train Images'
test_data_dir = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Test Images'

batch_size = 32
num_classes = len(Train_final_df['Image_class'].unique())  

condition = Train_final_df['Image_class'] == 'car'  
class_indices = np.where(condition)[0]
class_labels = Train_final_df['Image_class'].iloc[class_indices].tolist()

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=class_labels)

validation_classes = sorted(os.listdir(test_data_dir))

validation_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes)

model_1 = Sequential()
model_1.add(Conv2D(16, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Conv2D(32, (3, 3), activation='relu'))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Conv2D(128, (3, 3), activation='relu'))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Flatten())
model_1.add(Dense(64, activation='relu'))
model_1.add(Dense(num_classes, activation='softmax'))

model_1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

history_1 = model_1.fit(
    train_generator,
    steps_per_epoch=len(Train_final_df) // batch_size,
    epochs=10,  
    validation_data=validation_generator,
    validation_steps=len(Train_final_df) // batch_size)

Found 8144 images belonging to 196 classes.
Found 8041 images belonging to 196 classes.
Epoch 1/10
254/254 [==============================] - ETA: 0s - loss: 5.2587 - accuracy: 0.0062WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 254 batches). You may need to use the repeat() function when building your dataset.
254/254 [==============================] - 291s 1s/step - loss: 5.2587 - accuracy: 0.0062 - val_loss: 5.1854 - val_accuracy: 0.0097
Epoch 2/10
254/254 [==============================] - 203s 800ms/step - loss: 5.1590 - accuracy: 0.0128
Epoch 3/10
254/254 [==============================] - 204s 804ms/step - loss: 5.0984 - accuracy: 0.0144
Epoch 4/10
254/254 [==============================] - 204s 805ms/step - loss: 4.9845 - accuracy: 0.0255
Epoch 5/10
254/254 [==============================] - 209s 824ms/step - loss: 4.8028 - accuracy: 0.0475
Epoch 6/10
254/254 [==============================] - 196s 771ms/step - loss: 4.5783 - accuracy: 0.0690
Epoch 7/10
254/254 [==============================] - 197s 775ms/step - loss: 4.3333 - accuracy: 0.0897
Epoch 8/10
254/254 [==============================] - 196s 770ms/step - loss: 4.0716 - accuracy: 0.1312
Epoch 9/10
254/254 [==============================] - 194s 764ms/step - loss: 3.8233 - accuracy: 0.1658
Epoch 10/10
254/254 [==============================] - 196s 771ms/step - loss: 3.5496 - accuracy: 0.2161

Lets save the Model and Model Weights

model_1.save('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1.h5') 
model_1.save_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1_weights.h5')
loaded_model_1 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1.h5')
loaded_model_1.load_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1_weights.h5')

Lets analyze the model 1 performance on our Test Data

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes  
)
test_loss, test_acc = loaded_model_1.evaluate(test_generator)
print("Test Accuracy:", test_acc)
print("Test Loss:", test_loss)

Found 8041 images belonging to 196 classes.
252/252 [==============================] - 75s 299ms/step - loss: 5.2131 - accuracy: 0.0466
Test Accuracy: 0.04663598909974098
Test Loss: 5.213118076324463

Let plot the Loss as well as Accuracy for Model 1

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_1.history['loss']) + 1), history_1.history['loss'], label='Training Loss', marker='o')
plt.title('Model 1 - Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_1.history['accuracy']) + 1), history_1.history['accuracy'], label='Training Accuracy', marker='o')
plt.title('Model 1 - Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True)
plt.show()

Model 2

model_2 = Sequential()
model_2.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(Conv2D(64, (3, 3), activation='sigmoid'))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
# Number of Conventional Layers reduced

model_2.add(Flatten())
model_2.add(Dense(64, activation='sigmoid'))
model_2.add(Dense(num_classes, activation='sigmoid'))  # Activation Function for output layer changed

model_2.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) 
# Optimizer changed from adam to Standard-Gradient-Descent(SGD)

history_2 = model_2.fit(
    train_generator,
    steps_per_epoch=len(Train_final_df) // batch_size,
    epochs=5,  # Reduced epochs for faster training of basic model
    validation_data=validation_generator,
    validation_steps=len(Train_final_df) // batch_size)

Epoch 1/5
254/254 [==============================] - ETA: 0s - loss: 5.2871 - accuracy: 0.0053WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 254 batches). You may need to use the repeat() function when building your dataset.
254/254 [==============================] - 393s 2s/step - loss: 5.2871 - accuracy: 0.0053 - val_loss: 5.2779 - val_accuracy: 0.0055
Epoch 2/5
254/254 [==============================] - 275s 1s/step - loss: 5.2787 - accuracy: 0.0063
Epoch 3/5
254/254 [==============================] - 274s 1s/step - loss: 5.2787 - accuracy: 0.0065
Epoch 4/5
254/254 [==============================] - 277s 1s/step - loss: 5.2779 - accuracy: 0.0083
Epoch 5/5
254/254 [==============================] - 274s 1s/step - loss: 5.2779 - accuracy: 0.0083

Lets save the Model and Model Weights

model_2.save('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2.h5') 
model_2.save_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2_weights.h5')
loaded_model_2 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2.h5')
loaded_model_2.load_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2_weights.h5')

Lets analyze the model 2 performance on our Test Data

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes  
)
test_loss, test_acc = loaded_model_2.evaluate(test_generator)
print("Test Accuracy:", test_acc)
print("Test Loss:", test_loss)

Found 8041 images belonging to 196 classes.
252/252 [==============================] - 95s 375ms/step - loss: 5.2773 - accuracy: 0.0085
Test Accuracy: 0.008456659503281116
Test Loss: 5.277335166931152

Let plot the Loss as well as Accuracy for Model 2

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_2.history['loss']) + 1), history_2.history['loss'], label='Training Loss', marker='o')
plt.title('Model 1 - Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_2.history['accuracy']) + 1), history_2.history['accuracy'], label='Training Accuracy', marker='o')
plt.title('Model 1 - Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True)
plt.show()

Model 3

model_3 = Sequential()
model_3.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(64, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(128, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(256, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(512, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Flatten())
model_3.add(Dense(512, activation='relu'))
model_3.add(Dropout(0.5))
model_3.add(Dense(num_classes, activation='softmax'))

model_3.compile(loss='categorical_crossentropy', optimizer='adagrad', metrics=['accuracy'])

early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)

history_3 = model_3.fit(
    train_generator,
    steps_per_epoch=len(train_generator),
    epochs=10,  
    validation_data=validation_generator,
    validation_steps=len(validation_generator),
    callbacks=[early_stopping])

Epoch 1/10
255/255 [==============================] - 435s 2s/step - loss: 5.2806 - accuracy: 0.0047 - val_loss: 5.2779 - val_accuracy: 0.0057
Epoch 2/10
255/255 [==============================] - 433s 2s/step - loss: 5.2789 - accuracy: 0.0048 - val_loss: 5.2768 - val_accuracy: 0.0073
Epoch 3/10
255/255 [==============================] - 434s 2s/step - loss: 5.2772 - accuracy: 0.0070 - val_loss: 5.2763 - val_accuracy: 0.0056
Epoch 4/10
255/255 [==============================] - 430s 2s/step - loss: 5.2775 - accuracy: 0.0048 - val_loss: 5.2759 - val_accuracy: 0.0065
Epoch 5/10
255/255 [==============================] - 434s 2s/step - loss: 5.2766 - accuracy: 0.0056 - val_loss: 5.2756 - val_accuracy: 0.0075
Epoch 6/10
255/255 [==============================] - 432s 2s/step - loss: 5.2767 - accuracy: 0.0070 - val_loss: 5.2753 - val_accuracy: 0.0073
Epoch 7/10
255/255 [==============================] - 432s 2s/step - loss: 5.2757 - accuracy: 0.0061 - val_loss: 5.2751 - val_accuracy: 0.0075
Epoch 8/10
255/255 [==============================] - 430s 2s/step - loss: 5.2754 - accuracy: 0.0063 - val_loss: 5.2747 - val_accuracy: 0.0078
Epoch 9/10
255/255 [==============================] - 433s 2s/step - loss: 5.2761 - accuracy: 0.0076 - val_loss: 5.2745 - val_accuracy: 0.0085
Epoch 10/10
255/255 [==============================] - 432s 2s/step - loss: 5.2752 - accuracy: 0.0060 - val_loss: 5.2742 - val_accuracy: 0.0085

Lets save the Model and Model Weights

model_3.save('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3.h5') 
model_3.save_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3_weights.h5')
loaded_model_3 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3.h5')
loaded_model_3.load_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3_weights.h5')

Lets analyze the model 3 performance on our Test Data

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes  
)
test_loss, test_acc = loaded_model_3.evaluate(test_generator)
print("Test Accuracy:", test_acc)
print("Test Loss:", test_loss)

Found 8041 images belonging to 196 classes.
252/252 [==============================] - 116s 458ms/step - loss: 5.2742 - accuracy: 0.0085
Test Accuracy: 0.008456659503281116
Test Loss: 5.274209499359131

Let plot the Training and Validation Loss as well as Training and Validation Accuracy for Model 3

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_3.history['loss']) + 1), history_3.history['loss'], label='Training Loss', marker='o')
plt.title('Model 1 - Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_3.history['accuracy']) + 1), history_3.history['accuracy'], label='Training Accuracy', marker='o')
plt.title('Model 1 - Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True)
plt.show()

Lets compare and evaluate the performace of all the 3 CNN models

train_acc_1 = history_1.history['accuracy']
test_acc_1 = history_1.history['val_accuracy']
train_loss_1 = history_1.history['loss']
test_loss_1 = history_1.history['val_loss']

train_acc_2 = history_2.history['accuracy']
test_acc_2 = history_2.history['val_accuracy']
train_loss_2 = history_2.history['loss']
test_loss_2 = history_2.history['val_loss']

train_acc_3 = history_3.history['accuracy']
test_acc_3 = history_3.history['val_accuracy']
train_loss_3 = history_3.history['loss']
test_loss_3 = history_3.history['val_loss']

final_train_acc_1 = train_acc_1[-1]
final_test_acc_1 = test_acc_1[-1]
final_train_loss_1 = train_loss_1[-1]
final_test_loss_1 = test_loss_1[-1]

final_train_acc_2 = train_acc_2[-1]
final_test_acc_2 = test_acc_2[-1]
final_train_loss_2 = train_loss_2[-1]
final_test_loss_2 = test_loss_2[-1]

final_train_acc_3 = train_acc_3[-1]
final_test_acc_3 = test_acc_3[-1]
final_train_loss_3 = train_loss_3[-1]
final_test_loss_3 = test_loss_3[-1]

metrics_data = {
    'Model': ['Model 1', 'Model 2', 'Model 3'],
    'Train Accuracy': [final_train_acc_1, final_train_acc_2, final_train_acc_3],
    'Test Accuracy': [final_test_acc_1, final_test_acc_2, final_test_acc_3],
    'Train Loss': [final_train_loss_1, final_train_loss_2, final_train_loss_3],
    'Test Loss': [final_test_loss_1, final_test_loss_2, final_test_loss_3]
}

metrics_df = pd.DataFrame(metrics_data)
metrics_df

	Model	Train Accuracy	Test Accuracy	Train Loss	Test Loss
0	Model 1	0.216100	0.009700	3.549627	5.185400
1	Model 2	0.008259	0.005472	5.277876	5.277879
2	Model 3	0.006017	0.008457	5.275213	5.274206

Insights and Observations :

---

Model 1:

• Train Accuracy: 21.61%
• Test Accuracy: 0.97%
• Train Loss: 3.55
• Test Loss: 5.19
• This model demonstrates a notably higher train accuracy compared to the test accuracy, indicating potential overfitting. The high discrepancy between the train and test accuracies suggests that the model may not generalize well to unseen data.

---

Model 2:

• Train Accuracy: 0.83%
• Test Accuracy: 0.55%
• Train Loss: 5.28
• Test Loss: 5.28
• Both the train and test accuracies are quite low, suggesting that this model is not capturing the patterns in the data effectively. The similar train and test losses indicate that the model is not overfitting, but it is not learning the underlying patterns well.

---

Model 3:

• Train Accuracy: 0.60%
• Test Accuracy: 0.85%
• Train Loss: 5.28
• Test Loss: 5.27
• Similar to Model 2, this model also shows low train and test accuracies. However, the test accuracy is comparatively higher than the train accuracy, indicating that it might be performing slightly better on unseen data.

---

Overall Assessment:

• All three models exhibit low test accuracies, suggesting that they are not effectively capturing the patterns in the data.
• Model 1 shows signs of overfitting, while Models 2 and 3 display poor performance on both the train and test sets.

---

Methods of Optimization:

• The activation function (sigmoid) and optimizer (SGD) selected by Model 2 for the output layer are different from those of the other models (softmax, Adam and Adagrad).
• The variations in model performance that have been reported may be attributed to these changes in optimization strategies.

Let's now, at last, see how well each of our three models has done in terms of classifying the car photos.

Model 1

num_images_to_test = 5
current_index = 0

while current_index < len(test_images):
    actual_label = test_labels[current_index].argmax()
    predictions = loaded_model_1.predict(test_images[current_index].reshape(1, img_width, img_height, 3))
    predicted_label = predictions[0].argmax()
    plt.imshow(test_images[current_index])
    plt.title(f'Actual: {actual_label}, Predicted: {predicted_label}')
    plt.show()
    choice = input("Press 'n' to see the next image, or 'q' to quit: ")
    if choice == 'n':
        current_index += 1
    elif choice == 'q':
        break
    else:
        print("Invalid choice. Please try again.")

print("End of interactive image display.")

1/1 [==============================] - 0s 86ms/step

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1/1 [==============================] - 0s 56ms/step

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1/1 [==============================] - 0s 35ms/step

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1/1 [==============================] - 0s 36ms/step

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1/1 [==============================] - 0s 35ms/step

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1/1 [==============================] - 0s 35ms/step

Press 'n' to see the next image, or 'q' to quit: q
End of interactive image display.

Model 2

num_images_to_test = 5
current_index = 0

while current_index < len(test_images):
    actual_label = test_labels[current_index].argmax()
    predictions = loaded_model_2.predict(test_images[current_index].reshape(1, img_width, img_height, 3))
    predicted_label = predictions[0].argmax()
    plt.imshow(test_images[current_index])
    plt.title(f'Actual: {actual_label}, Predicted: {predicted_label}')
    plt.show()
    choice = input("Press 'n' to see the next image, or 'q' to quit: ")
    if choice == 'n':
        current_index += 1
    elif choice == 'q':
        break
    else:
        print("Invalid choice. Please try again.")

print("End of interactive image display.")

1/1 [==============================] - 0s 40ms/step

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1/1 [==============================] - 0s 45ms/step

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1/1 [==============================] - 0s 42ms/step

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1/1 [==============================] - 0s 44ms/step

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1/1 [==============================] - 0s 46ms/step

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1/1 [==============================] - 0s 36ms/step

Press 'n' to see the next image, or 'q' to quit: q
End of interactive image display.

Model 3

num_images_to_test = 5
current_index = 0

while current_index < len(test_images):
    actual_label = test_labels[current_index].argmax()
    predictions = loaded_model_3.predict(test_images[current_index].reshape(1, img_width, img_height, 3))
    predicted_label = predictions[0].argmax()
    plt.imshow(test_images[current_index])
    plt.title(f'Actual: {actual_label}, Predicted: {predicted_label}')
    plt.show()
    choice = input("Press 'n' to see the next image, or 'q' to quit: ")
    if choice == 'n':
        current_index += 1
    elif choice == 'q':
        break
    else:
        print("Invalid choice. Please try again.")

print("End of interactive image display.")

1/1 [==============================] - 0s 27ms/step

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1/1 [==============================] - 0s 38ms/step

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1/1 [==============================] - 0s 27ms/step

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1/1 [==============================] - 0s 27ms/step

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1/1 [==============================] - 0s 28ms/step

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1/1 [==============================] - 0s 29ms/step

Press 'n' to see the next image, or 'q' to quit: q
End of interactive image display.

------------End of Project-Milestone 1------------